Journal:Design of a data management reference architecture for sustainable agriculture

Abstract

Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.

Keywords: sustainability, agriculture, sustainable agriculture, data management, reference architecture, design science research

Introduction

The increase in food demand and its associated large ecological footprint call for action in agricultural production. [1] Inputs and assets should be optimized, and long-term ecological impacts should be assessed for sustainable agriculture. Decision-making processes on optimization and assessment need data on several inputs, outputs, and external factors. To this end, various systems have been developed for data acquisition and management to enable precision agriculture. [1] Precision agriculture refers to the application of technologies and principles for improving crop performance and environmental sustainability. [2] Smart farming extends precision agriculture and enhances decision-making capabilities by using recent technologies for smart sensing, monitoring, analysis, planning, and control. [1] Data to be acquired are enhanced by context, situation, and location awareness. [1] Real-time sensors are utilized to collect various data, and real-time actuators are used to fine-tune production parameters instantly.

In the late 2000s, Murakami et al. [3] and Steinberger et al. [4] pointed out a need for data storage and a processing platform for agricultural production. They utilized web services to send and receive data from a central web application. That web application received, stored, and processed data, and it provided the required outputs to its users or any other system. Similarly, Sørensen et al. [5] listed several data processing use cases to assist farmers’ decision-making processes. More recently, technologies such as the internet of things (IoT) make digital data acquisition, and hence smart farming, possible. [6] In recent years, many studies have been performed in the fields of smart farming and precision agriculture. [7,8,9,10,11,12,13] At the heart of many of those studies is Industry 4.0, which acts as a transformative force on smart farming processes. Industry 4.0-related technologies—namely IoT, big data, edge computing, 3D printing, augmented reality, collaborative robotics, data science, cloud computing, cyber-physical systems, digital twins, cybersecurity, and real-time optimization—are increasingly integrated into different parts of modern agricultural systems. [14]

To realize operational efficiency, full automation, and high productivity in these systems, different types of data are collected from operational systems using different sensors, stored in big data systems, and processed using machine learning and deep learning approaches. Traditional data management techniques and systems are not sufficient to deal with this scale of data, and as such, big data infrastructures and systems have been designed and implemented. To manage the complexity of this big data, many different aspects of data must be considered during the design of these systems. Different data management reference architectures have been designed to date. [15,16,17] To the best of our knowledge, none of these studies have focused on sustainable agriculture. There exist several practices for sustainable agriculture that can protect the environment, improve soil fertility, and increase natural resources. It is known that agriculture can affect soil erosion, water quality, human health, and pollination services. [18] As such, sustainable agriculture is crucial to minimize the negative effects of agricultural production. Sustainable agriculture requires an iterative process because each actor in the system has a different responsibility, and the success of this process is highly dependent on the success of each actor.

The goal of this study is to present a data management reference architecture for supporting smart farming, sustainable agriculture, and other domains. The study builds on the recent developments in data management and processing, i.e., big data, machine learning, and data lakes. We designed a data management reference architecture for sustainable agriculture and evaluated it using several case studies. Domain scoping, domain modeling, and reference architecture design stages were followed to create the reference architecture. Based on the reference architecture, we can design different application architectures. During the validation stage of this study, using different case studies obtained from the literature, we have shown the applicability of our reference architecture as a novel data management reference architecture for sustainable agriculture.

The structure of this paper follows the outline proposed by Gregor & Hevner [19] for design science research. The next section summarizes the research method adopted in this study, followed by the definition and structuring of the problem by analyzing the existing literature. We then present the related reference architecture studies and explain the solution design process and the reference architecture obtained. That is followed by the evaluation of the reference architecture by deriving application architectures from it based on some requirements from the sustainable agriculture domain. The penultimate section discusses the results, and the final section provides conclusions and plans of future work.

Research method

The design science research (DSR) method proposed by Hevner et al. [20] was followed in this study. DSR is a problem-solving paradigm and seeks to create artifacts through which information systems can be effectively and efficiently engineered. [20] These artifacts are designed to interact with a problem context to improve something in that context. [21]

The activities and the artifacts span two significant dimensions, i.e., problem-solution and theory-practice dimensions. [22] Figure 1 shows the research method used in this study. The first step was the identification of some problem instances occurring in practice and sharing similar aspects. These problem instances were analyzed, and a problem statement was formed using theoretical concepts from the literature. A conceptual solution, i.e., an artifact or artifacts, was designed by following a systematic approach. Domain analysis was used to derive and represent domain knowledge to be used for solution design. Domain analysis involved domain scoping and domain modeling activities. [23] Domain scoping refers to the identification of relevant knowledge sources to derive the key concepts of the solution. [24] To this end, several searches were conducted on the Scopus database using different search strings. Domain modeling aims at unifying and representing the domain knowledge obtained from relevant sources. The feature model was used to represent the output of domain modeling. [25] A reference architecture was designed as a conceptual solution.

Fig. 1 The research method used in this study, which involves the main steps of design science research (DSR), i.e., problem definition, solution design, and validation. The iterative nature of the research method was neglected for the sake of simplicity.

To evaluate the reference architecture, requirements were specified using recent literature on sustainable agriculture. [26,27,28,29,30] Based on these requirements, a concrete application architecture was derived using the reference architecture.

In accordance with DSR and the research method described here, the following sections describe problem definition, design of a solution, and the evaluation of the solution.

Problem definition

This study was motivated by three use cases involving different data management requirements to support sustainable agriculture. The following three use cases were used for understanding and conceptualizing the problem:

• Case 1: Satellite images (e.g., Sentinel-2 data) can be obtained from a data provider. These images can be processed to derive plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content during the growing season. [31] Afterward, the current growth status and development of cultivated crops at each location in the field can be deduced. [32] This information can be used for site-specific plant protection and fertilization measures [33], which support sustainable agriculture.

• Case 2: Harvested crop volume can be quantified and recorded in real time using numerous sensors. [34] Various parameters such as "quantity per hectare" and "flow" can be calculated, and crop productivity maps can be built. [34] Farmers can use these maps to optimize inputs such as fertilizers, pesticides, and seeding rates, resulting in an increase in yields. [35]

• Case 3: Machinery process data such as speed, angle, pressure, and flow rate can be obtained through sensors in tractors and equipment. [4] Machine, worker, field, and time slot data can be stored, and basic statistics like minimum, maximum, and standard deviation can be computed. [4] As a result, automated documentation of the production process and site-specific work can be attained. [4]

Table 1 summarizes the above-mentioned cases from a data management perspective. Similar to many cases in various domains, at a high level, digital data are produced and fed to a software system to be processed and stored. Such a system can be designated a data management platform and produce outputs that can lead to better business outcomes. As per the first case, satellite images can be processed via computer vision algorithms to drive plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content, which can in turn be used to track the current growth status of cultivated crops and support decision-making activities.

Table 1. A summary of the three cases presented above from a data management perspective Data input Data processing Data output Outcome Satellite images Derive plant parameters via computer vision algorithms Plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content Track current growth status and development of cultivated crop at each location Harvested crops volume via sensors Build crop productivity maps Various parameters such as "quantity per hectare" and "flow" on the map Use such maps to optimize inputs such as fertilizers, pesticides, and seeding rates in order to increase yields Machinery process data via sensors Compute statistics Machine, worker, field, and time slot data, along with basic statistics such as minimum, maximum, and standard deviation Attain automated documentation of the production process and site-specific work

Solution design and artifact description

This section starts with a summary of related reference architecture studies and then presents the three steps of the solution design phase, namely domain scoping, domain modeling, and reference architecture design.

Related reference architecture studies

Before presenting our reference architecture, we discuss the available reference architectures in the literature. First, while Nikkilä et al. [36] and Kaloxylos et al. [37] presented architectural aspects of Farm Management Information Systems (FMISs), they did not propose a reference architecture, which limited the utility of their research for our purposes. However, Tummers et al. [17] designed a reference architecture for FMISs. They first identified the stakeholders and their concerns. Afterward, a feature model for FMISs was created. The reference architecture was designed and represented via context and decomposition views. Three case studies were performed to show the applicability of the proposed reference architecture.

Köksal & Tekinerdogan [6] proposed a reference architecture for IoT-based FMISs. They proposed an architecture design method and showed that the approach is practical and effective. Their architecture included data acquisition, data processing, data visualization, system management, and external services. Each main feature consisted of several sub-features. For instance, the data processing feature involved sub-features like image/video processing, data mining, decision support, and data logging. They used decomposition, layered, and deployment views to document the reference architecture. For deriving a concrete FMIS architecture, their reference architecture can thus be used.

Kruize et al. [38] proposed a reference architecture for farm software ecosystems. Farm software ecosystems aim to fulfill the needs of several actors in the smart farming domain. In that respect, their scope is much wider compared to FMISs. The farm software ecosystem reference architecture mainly focused on the problem of bringing various software and hardware components together to form a platform for multiple actors.

To the best of our knowledge, there is no other study that presents a data management reference architecture for sustainable agriculture. Although some of the previous studies mention several data-related components, a complete architectural view of managing data for sustainable agriculture was missing. As such, our reference architecture study aims to fill this research gap.

Domain scoping

The Scopus database was used as the knowledge source for domain scoping. To identify the search keywords, it was crucial to understand the recent factors driving reference architectures for data management. The concept of "big data" has emerged to highlight challenges of data management, including volume, velocity, and variety. [39] Machine learning is another hot research topic, which tries to acquire knowledge by extracting patterns from raw data [40,41] and solve problems using this knowledge. The concept of a "data lake" is another to have recently emerged, which addresses the shortcomings of data warehouses. A data lake can be defined as a data management platform that allows the storing of both structured and unstructured data, unlike data warehouses that handle only structured data. This type of platform is designed to enable big data processing, real time analytics, and machine learning.

Based on these recent trends, four search strings were used to form the initial paper pool. The phrase “reference architecture” was combined with four phrases representing the recent trends in data processing for sustainable agriculture (i.e., "data management," "big data," "machine learning," and "data lake"). The search keywords were kept general to have a high recall and relatively low precision. Although this required more effort from the authors, obtaining a broader initial set of papers decreased the possibility of missing relevant studies.

The database search on Scopus was conducted in February 2021. No criterion was set for the publication date. A total number of 270 papers was obtained for the pool of candidate papers. All the results were combined in an Excel sheet, which included useful information about the papers such as title, abstract, keywords, and publication date, which are used in further steps.

To identify the relevant papers for designing a reference architecture, we applied the exclusion criteria to the papers obtained from Scopus. Those papers that were duplicated, not written in English, or lacking full text were filtered out. The papers involving a reference architecture to process data in any business domain were included. Seven papers included a reference architecture for data processing, along with the essential components.

The data extraction phase followed the selection of relevant papers. The components of the data processing architecture listed in the papers were extracted and recorded in an Excel sheet. These components were unified by reading the definitions presented in the papers. Table 2 shows the unified list of the components and the source papers where each component was identified.

Table 2. The unified list of components identified in the literature Component Nadal et al.[15] Dayal et al.[42] Suriarachchi and Plale[43] Rao et al.[44] Sang et al.[45] Arass et al.[46] Pääkkönen and Pakkala[47] Ingestion ✓ ✓ ✓ ✓ ✓ ✓ ✓ Information extraction ✓ Data quality management ✓ ✓ ✓ ✓ ✓ Integration ✓ ✓ ✓ ✓ Analysis ✓ ✓ ✓ ✓ ✓ ✓ ✓ Storage ✓ ✓ ✓ ✓ ✓ ✓ ✓ Security/Privacy ✓ ✓ Metadata management ✓ Replication/Archiving ✓ ✓ ✓

All the papers address three main components that deal with collecting data from various sources (i.e., acquisition), processing data to provide some value to data consumers (i.e., analysis), and addressing persist data (i.e., storage). Pääkkönen and Pakkala [47] identified information extraction as a component to extract structured data from unstructured and semi-structured data, such as email or images. Data quality management is another vital component to handle data quality problems. Data received from various sources should be integrated for further analysis. Security and privacy components are used to protect data from unauthorized data and for proper handling of personal data. Metadata management refers to the creation and storage of metadata to document the meaning of data. The replication component ensures redundant storage of data sources to provide better data availability in case of technical problems, and the archiving component is responsible for storing cold data for future possible needs.

Domain modeling

Feature modeling is one of the approaches to represent domain knowledge in a reusable format. [24,25] Figure 2 shows the feature model, which is derived from the unified list of components presented in Table 2. A feature diagram can include mandatory and optional features. Three components identified by all papers are treated as mandatory features. The remaining features are optional and can be used depending on business requirements.

Fig. 2 The feature model for data management in the sustainable agriculture domain

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.